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Publication# Iterative simulation and optimization approach for energy performance evaluation of ground source heat pump systems

Résumé

The energy transition in Switzerland specifies reduction of the country’s primary energy consumption and greenhouse gas emissions. In the building sector, one way to achieve this is through adoption of efficient and sustainable technologies. With this aim, the building energy systems are gaining complexity. Modelling tools are becoming increasingly important in the design and evaluation of such systems. Two modelling methods are commonly used, simulation and optimization, which substantially differ in their approach and purpose. Simulation is a descriptive tool used to virtually represent systems’ behaviour under given conditions and operation strategies. Optimization approaches explore the possible scenarios under given system limits; they determine the best solution by optimizing an objective function described in mathematical form. In this project, the interactions and complementary use of these methods are investigated through evaluation of the newly installed energy systems of multi-familiy houses built in Zurich. The systems include renewable sources from solar and geothermal energy. The main components are a heat pump coupled with a borehole heat exchanger (BHE) and solar photovoltaic panels (PV) or hybrid photovoltaic panels (PV/T) for electricity production. Two system variants including different rates of borehole regeneration resulting from free cooling of the houses or injection of heat produced by PV/T are evaluated. The systems are divided into interlinked blocks representing the main components of the system: the borehole heat exchanger and surrounding ground, the heat pump, the storage tanks, the piping systems and the photovoltaic installations. The blocks are simulated with an hourly time scale. Energy consumption, self-consumption of on site produced electricity as well as performance degradation due to the long term operation are some of the results obtained from simulation. Monitoring data are used for calibration and validation of the simulation model. Separate optimization models of the evaluated energy systems are then developed. They are improved based on system characteristics obtained from the simulation model. In this way, optimal operation strategies which take into account specific operational limits are identified. Different levels of precision of parameters integration from the simulation results (constant over the year and hourly defined) are implemented and the influences on the optimization results are investigated. The results of the optimization are subsequently implemented in the simulation model. The results of the simulation found that the boreholes are sustainably exploited due the conservative design of the system according the simulated operation conditions. The influence of the regeneration is noticeable, however the simulation of the long term operation of a hypothetical variant without regeneration induces only a limited efficiency decrease and can also be considered as sustainable. The optimization results showed that the self consumption of electricity produced on site can be significantly improved by adapting the heat pump production profile with the electricity production; this was achieved through judicious management of the storage tanks. The analysis of the different level of parameter integration in the optimization model revealed that the optimization results are sensitive to the level of precision of the parameters. The iterative process allowed to combine the strengths of both modelling methods: the simulation was used to precisely describe the systems behaviour. Operational profiles from optimization were subsequently used in the simulation. The outcome of this process helped to better understand the system, identify optimal operating strategies while considering system limitations, as well as presenting

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Simulation de phénomènes

La simulation de phénomènes est un outil utilisé dans le domaine de la recherche et du développement. Elle permet d'étudier les réactions d'un système à différentes contraintes pour en déduire les résultats recherchés en se passant d'expérimentation. Les systèmes technologiques (infrastructures, véhicules, réseaux de communication, de transport ou d'énergie) sont soumis à différentes contraintes et actions. Le moyen le plus simple d'étudier leurs réactions serait d'expérimenter, c'est-à-dire d'exercer l'action souhaitée sur l'élément en cause pour observer ou mesurer le résultat.

Parameter

A parameter (), generally, is any characteristic that can help in defining or classifying a particular system (meaning an event, project, object, situation, etc.). That is, a parameter is an element of a system that is useful, or critical, when identifying the system, or when evaluating its performance, status, condition, etc. Parameter has more specific meanings within various disciplines, including mathematics, computer programming, engineering, statistics, logic, linguistics, and electronic musical composition.

Hyperparameter optimization

In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned. The same kind of machine learning model can require different constraints, weights or learning rates to generalize different data patterns.

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